自然语言处理，能否成为人工智能与人类语言完美交互的答案？

from collections import defaultdict, Counter

def generate_ngrams(text, n):
    tokens = text.split()
    ngrams = []
    for i in range(len(tokens) - n + 1):
        ngram = ' '.join(tokens[i:i+n])
        ngrams.append(ngram)
    return ngrams

text = "自然语言处理是人工智能的重要分支"
bigrams = generate_ngrams(text, 2)
bigram_counts = Counter(bigrams)
print(bigram_counts)

from sklearn import svm
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.datasets import fetch_20newsgroups
from sklearn.model_selection import train_test_split

# 加载数据集
categories = ['alt.atheism', 'soc.religion.christian']
news = fetch_20newsgroups(subset='train', categories=categories)
X = news.data
y = news.target

# 特征提取
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(X)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练SVM模型
clf = svm.SVC(kernel='linear')
clf.fit(X_train, y_train)

# 预测
y_pred = clf.predict(X_test)

import torch
import torch.nn as nn

class SimpleRNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(SimpleRNN, self).__init__()
        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        out, _ = self.rnn(x)
        out = self.fc(out[:, -1, :])
        return out

# 示例参数
input_size = 10
hidden_size = 20
output_size = 2
model = SimpleRNN(input_size, hidden_size, output_size)

# 随机输入
x = torch.randn(1, 5, input_size)
output = model(x)

import torch
import torch.nn as nn

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTMModel, self).__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        out, _ = self.lstm(x)
        out = self.fc(out[:, -1, :])
        return out

# 示例参数
input_size = 10
hidden_size = 20
output_size = 2
model = LSTMModel(input_size, hidden_size, output_size)

# 随机输入
x = torch.randn(1, 5, input_size)
output = model(x)

import torch
import torch.nn as nn

class TextCNN(nn.Module):
    def __init__(self, vocab_size, embedding_dim, num_filters, filter_sizes, output_size):
        super(TextCNN, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.convs = nn.ModuleList([
            nn.Conv2d(1, num_filters, (fs, embedding_dim)) for fs in filter_sizes
        ])
        self.fc = nn.Linear(num_filters * len(filter_sizes), output_size)

    def forward(self, x):
        x = self.embedding(x)
        x = x.unsqueeze(1)
        x = [torch.relu(conv(x)).squeeze(3) for conv in self.convs]
        x = [torch.max_pool1d(i, i.size(2)).squeeze(2) for i in x]
        x = torch.cat(x, 1)
        x = self.fc(x)
        return x

# 示例参数
vocab_size = 1000
embedding_dim = 100
num_filters = 100
filter_sizes = [3, 4, 5]
output_size = 2
model = TextCNN(vocab_size, embedding_dim, num_filters, filter_sizes, output_size)

# 随机输入
x = torch.randint(0, vocab_size, (1, 20))
output = model(x)

from transformers import BertTokenizer, BertForSequenceClassification
import torch

# 加载预训练模型和分词器
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
model = BertForSequenceClassification.from_pretrained('bert-base-chinese', num_labels=2)

# 示例文本
text = "自然语言处理很有趣"
inputs = tokenizer(text, return_tensors='pt')

# 前向传播
outputs = model(**inputs)
logits = outputs.logits
predicted_class_id = logits.argmax().item()